BFSI Sector and Data Science
BFSI or Banking, Financial Services, and Insurance is the industry’s terminology for the companies which provide a range of financial products and services. These include universal banks that provide a range of financial services or companies that operates in one or more of these financial sectors. BFSI includes commercial banks, non-banking financial companies, insurance companies, pensions funds, cooperatives, other smaller financial entities, mutual funds institutions etc. The term is referred to by information technology (IT), business process outsourcing (BPO) companies, and firms that manage data processing, software development activities, and application testing.
The banking includes core banking, retail banking, private banking, corporate banking, investment banking, and credit cards.
Financial services include mutual funds, payment gateways, and stock-broking. Insurance covers both general insurance and life insurance.
Data Science: Data Science is the process of analyzing data for making a decision that could relate to business or marketing or any operational decisions. It is a process of applying various scientific methods to raw data and deriving meaningful insights from the data. This is not a single domain but a multidisciplinary domain, which analyzes the data and finds the best solutions based on the data. To get the most insightful and complete answer to your questions, it includes all types of data.
Data Science in Banking and Data Science in Finance
Data Science in Banking: The processes of Data Science in the banking sector is changing rapidly in the face of the banking industry. It is becoming a trend and is one of the necessities to keep up in today’s world of competition. Almost all banks are in search of better ways of advanced technology that can help in understanding their customers. Banks are trying to increase customer loyalty by providing more efficient operational efficiency, making smarter decisions, and improving performance. The banks are trying to identify different patterns from the available data for better interactions with their customers.
With Data Science in Banking, banks use the data from the previous history trends, customer transactions, communications, and loyalty. This data is unstructured which is difficult to deal with, extracting insights from such a large set of data is a great challenge. Various data analysis methods can be used for this purpose like data fusion and integration, natural language processing, machine learning, signal processing, etc.
Data Science in Finance: Finance industry is a constantly growing industry. Data Science is quite helpful when we use a combination of statistics, mathematics, graph charts, visualization, communication, and complex systems. It helps them to discover the advanced requirements in various areas in the financial industry and, helps to reshape their business strategies. This relies on scientific methods and algorithms to extract meaningful insights from both structured and unstructured data. Data Science in finance is an interdisciplinary field that generates new opportunities for financial industries. By enabling the application of scientific techniques like predictive modeling, data wrangling, clustering, dimensionality reduction, and visualization. The finance industry uses Data Science by minimizing the efforts and time taking tasks in managing various financial activities.
Artificial Intelligence in BFSI Sector
Artificial intelligence (AI) has great potential to transform the banking, financial services, and insurance (BFSI) industry. Adoption of Artificial intelligence in BFSI firms plays a vital role in allowing the banking industry which can help meet strategic objectives like enhancing customer experience, delivering personalized service, cost, and efficiency optimization, and improving speed-to-market for offerings. Artificial intelligence provides real-time insights with actionable information based on past activity and data. The speed, agility, and flexibility offered by artificial intelligence are unique. Artificial intelligence can offer user-friendly services via online portals or smartphone apps that make transactions in financial procedures extremely quick, easy, and convenient. Artificial intelligence helps by enabling smooth workflows at optimal costs and minimal time to improve the customer experience. AI-powered chatbots have helped the banking industry by reducing the resolution time. This helps to optimize operational efficiency and improve customer experience using artificial intelligence as their preferred technology tool to identify theft, fraud, and security breaches and to detect errors and eliminate them. This helps in customer trust and loyalty. Few companies are using artificial intelligence for creating a personalized payment experience for their customers. Artificial Intelligence and cloud-based technologies help banks and financial institutions to develop tech-savvy marketing strategies enabling them to delight the customers and cross-sell or upsell related products and services to the right customers. Risk analysis helps them to expedite decision-making by providing approvals for faster workflows.
Few Key business challenges in BFSI Sector
The BFSI sector has experienced some profound changes, due to the rapid rise in technology. Below are a few key challenges faced by businesses in the BFSI sector:
Digital Adoption: One of the biggest issues in the industry also lies with the changing technology itself. Growth is very important, but they must spend money on technology to grow. They must invest in technology such as robotics and other artificial intelligence automation tools to increase their efficiency and reduce the cost which is associated with operational, compliance, and risk management. Modernizing their technology platforms and data storage will enable them to get big data solutions such as AI-supported digital customer support assistants. They must also consider by consolidating platforms they can also provide more efficient, customer-friendly experiences across all their channels like mobile, internet, and physical locations.
Increased Competition: Competition within the industry is still robust. The consumer wants more personalized and more automated services with easier access. The institutions which provide all these services will dominate their share in the market. Today, customers are not very concerned about brand loyalty and identity and whichever institutions provide the services will retain their customers.
Regulatory Challenges: Regulations in this industry continue to increase. They have to constantly evaluate and improve their operations so that they can keep up with fast-changing consumer and shareholders’ expectations, technology, and industry regulations. They must create a strategy to innovate and stay in compliance. Some of the key attributes to reformulating strategies are as follows:
• Geopolitical Change
• Data Protection and Governance
• Operational Resilience
• Credit Quality
• Capital and Liquidity Shifts
• Compliance Agility
• Financial Crime
• Customer Trust
• Ethical Conduct
Changing customer expectations: Due to the technological changes which have reached an increasingly dominant portion of consumer culture, customers’ expectations have also gone up. Customers want to receive personalized services. Modern customers are so concerned with personalization and high-quality care that it can even overtake their customer loyalty. Customers' feelings are more today towards the services they receive and the experience they encounter rather than the brand name.
Organizing Big data and Eliminating Data Breaches: Data is getting bigger because a lot of data is being generated by several sources. Big data is very necessary but also an obstacle to handle as the raw data is structured and unstructured in nature. The challenge the sector faces is to sort through all its data and determine what is useful and what is not useful. As the data is in huge these sectors are the prime targets for cybercrime. There are many instances in the sector as have faced data breaches as well.
A few areas where the BFSI sector can use Data Science and Artificial Intelligence
Detecting and Preventing Fraud: Data science is crucial in a customer database for gathering, summarizing, and forecasting fraudulent activity. Before the emergence of data science and big data to analyze customer records and providing reliable data was impossible. Advanced technologies like Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) now can assist and prevent fraud. With the help of a data scientist, customer security may be improved by monitoring and analyzing customers’ financial actions to detect any suspicious or harmful behaviour.
Customer Data Management: In today’s big data era, BFSI must handle a huge database. It is impossible to collect, analyze, and store a large amount of data. Thus, different firms are investing in data science, machine learning, natural language processing, and Artificial Intelligence tools and techniques to analyze the structured and unstructured data to a meaningful insight to understand their customers and come up with new revenue-generating tactics. Data scientists can learn more about customer behaviour, their priorities, and others by analyzing the data. This also enables them to create more efficient models with more accurate outcomes.
Customer Support: The key to a productive long-term relationship with your customer is to provide outstanding customer support service. Customer support is an essential part of customer service. Customer support includes responding to all the complaints and queries that a customer is facing, and it should be followed up on time. Data Science makes this customer support method more reliable, more efficient, more transparent, more sensitive, more effective, and less costly as regards employee time.
Customer Segmentation: Customer segmentation can be done with the help of Data Science based on their behaviour, or characteristics based on age, income, etc. Customer segmentation allows firms to use their time and resource more effectively. There are a whole lot of techniques a data scientist uses such as clustering, logistic regression, decision trees, and so on. By this, they can forecast the customer lifetime value for each segment. Customer segmentation is designed for improving customer service and helping in the loyalty and retention of each customer.
Risk Modeling: Data Science is used in financial firms to control various financial activities and to determine the suitable pricing for financial products. There are two different types of risk modeling available. Credit Risk Modeling enables to anticipate whether a client will be able to repay the loan by looking at the prior credit history and credit reports of the customer and, then the institution's analysis takes the decision. Investment Risk Modeling is used to identify hazardous assets by Investment banks. This enables them to advice better financial advice to the customer and makes smarter decisions to increase profits. Thus, Risk Modeling is very crucial in financial firms.
How FutureAnalytica can help in this journey
FutureAnalytica is the only holistic automated machine-learning, no-code AI platform providing end-to-end seamless data-science functionality with Data Lake, AI App Store & world-class Data-Scientist support, thus reducing time and effort in your data-science and AI journey.
With FutureAnalytica’s AI solutions for advanced financial monitoring in real-time powered by state-of-art algorithms, collection analytics, customer segmentation, credit risk modelling, customer acquisition and retention cross-selling, and up-selling. This way the BFSI sector can make smarter decisions, improve performances, and makes the work simpler.
To gain a competitive advantage, financial firms must acknowledge the crucial importance of data science, integrate it into their decision-making process, and develop strategies based on the actionable insights from their client’s data. For financial services all around the world, artificial intelligence, whether it is machine learning, deep learning, or a series of algorithms that can show an array of big data, is giving enterprises distinct strategic advantages. When a bank, lender, brokerage house, or payments system effectively uses Artificial Intelligence, they run more efficiently and can connect more effectively with their customers.
With FutureAnalytica AI-driven Automation, Banks, Financial Institutions and Insurance companies are leveraging machine capabilities to enhance operations, scale, and cut costs.
We hope this article was insightful and helped you to understand the importance of Data Science and Artificial Intelligence in the BFSI Sector providing useful and actionable AI-based decisions that can be taken for solving complex problems. Thank you for showing interest in our blog, if you have any questions related to Data Science, Data Analytics, Machine Learning, or AI-fueled data-led platforms, please send us an email at firstname.lastname@example.org.